首页|Large Language Model-Informed ECG Dual Attention Network for Heart Failure Risk Prediction

Large Language Model-Informed ECG Dual Attention Network for Heart Failure Risk Prediction

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  • NETL
  • NSTL
  • IEEE
Heart failure (HF) poses a significant public health challenge, with a rising global mortality rate. Early detection and prevention of HF could significantly reduce its impact. We introduce a novel methodology for predicting HF risk using 12-lead electrocardiograms (ECGs). We present a novel, lightweight dual attention ECG network designed to capture complex ECG features essential for early HF risk prediction, despite the notable imbalance between low and high-risk groups. This network incorporates a cross-lead attention module and 12 lead-specific temporal attention modules, focusing on cross-lead interactions and each lead's local dynamics. To further alleviate model overfitting, we leverage a large language model (LLM) with a public ECG-Report dataset for pretraining on an ECG-Report alignment task. The network is then fine-tuned for HF risk prediction using two specific cohorts from the U.K. Biobank study, focusing on patients with hypertension (UKB-HYP) and those who have had a myocardial infarction (UKB-MI). The results reveal that LLM-informed pre-training substantially enhances HF risk prediction in these cohorts. The dual attention design not only improves interpretability but also predictive accuracy, outperforming existing competitive methods with C-index scores of 0.6349 for UKB-HYP and 0.5805 for UKB-MI. This demonstrates our method's potential in advancing HF risk assessment with clinical complex ECG data.

ElectrocardiographyPredictive modelsFeature extractionLeadBiological system modelingData modelsTrainingRepresentation learningLarge language modelsHafnium

Chen Chen、Lei Li、Marcel Beetz、Abhirup Banerjee、Ramneek Gupta、Vicente Grau

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Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, U.K.|Imperial College London, London, U.K.|University of Sheffield, Sheffield, U.K.

Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, U.K.|University of Southampton, Southampton, U.K.

Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, U.K.

Novo Nordisk Research Centre Oxford (NNRCO), Oxford, U.K.

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2025

10.1109/TBDATA.2025.3536922

IEEE transactions on big data

IEEE transactions on big data

ISSN：
年,卷(期)：2025.11(3)
  • 63